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README.md 0 → 100644
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# Sa2VA
Sa2VA：将SAM2与LLaVA结合，从而实现对静态和动态视觉内容的扎实、多模态理解。
## 论文
`Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos`
- https://arxiv.org/abs/2501.04001
## 模型结构
<!-- 此处一句话简要介绍模型结构 -->
Sa2VA 是将基础视频分割模型 SAM-2 与高级视觉语言模型 LLaVA 相结合。基础模型结构都是Transformer结构。
<div align=center>
<img src="./doc/transformer.png"/>
<div >Transformer</div>
</div>
## 算法原理
Sa2VA是第一个对图像和视频进行密集理解的统一模型。与通常仅限于特定模态和任务的现有多模态大型语言模型不同，Sa2VA支持广泛的图像和视频任务，包括引用分割和对话，只需进行最小的一次指令调整。Sa2VA将基础视频分割模型SAM-2与高级视觉语言模型LLaVA相结合，将文本、图像和视频统一到一个共享的LLM令牌空间中。
<div align=center>
<img src="./doc/Sa2VA.png"/>
<div >Sa2VA</div>
</div>
## 环境配置
```
mv sa2va_pytorch sa2va # 去框架名后缀
# docker的-v 路径、docker_name和imageID根据实际情况修改
# pip安装时如果出现下载慢可以尝试别的镜像源
```
### Docker（方法一）
<!-- 此处提供[光源](https://www.sourcefind.cn/#/service-details)拉取docker镜像的地址与使用步骤 -->
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-ubuntu22.04-dtk24.04.3-py3.10 # 本镜像imageID为：0291c26699b0
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=16G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --network=host --name docker_name imageID bash
cd /your_code_path/sa2va
cd deme
pip install -r requirements.txt
```
### Dockerfile（方法二）
<!-- 此处提供dockerfile的使用方法 -->
```
cd /your_code_path/sa2va/docker
docker build --no-cache -t codestral:latest .
docker run -it -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro --shm-size=16G --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --network=host --name docker_name imageID bash
cd /your_code_path/sa2va
cd demo
pip install -r requirements.txt
```
### Anaconda（方法三）
<!-- 此处提供本地配置、编译的详细步骤，例如： -->
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```
DTK驱动: dtk24.04.3
python: python3.10
pytorch: 2.3.0
```
`Tips：以上DTK驱动、python、pytorch等DCU相关工具版本需要严格一一对应`
其它非深度学习库参照requirements.txt安装：
```
cd /your_code_path/sa2va
cd demo
pip install -r requirements.txt
```
## 数据集
该项目所需数据集较大，如下所示：
```
data/
├── video_datas
| ├── revos
| ├── mevis
| └── davis17
| └── chat_univi # video-chat data
| └── sam_v_full # please download this from sam-2 offical repp.
| └── sam_v_final_v3.json
├── ref_seg
| ├── refclef
| ├── refcoco
| ├── refcoco+
| ├── refcocog
| ├──
├── glamm_data
| ├── images
| ├── annotations
├── osprey-724k
| ├── Osprey-724K
| ├── coco
├── llava_data
| ├── llava_images
| ├── LLaVA-Instruct-150K
| ├── LLaVA-Pretrain
```
下载链接https://huggingface.co/datasets/Dense-World/Sa2VA-Training \
也可从SCNet上高速下载。
## 训练
本项目训练所需计算资源较大，建议至少8卡。
```
bash tools/dist.sh train projects/llava_sam2/configs/sa2va_4b.py 8
```
## 推理
方式一
通过gradio启用webui界面推理：
```
# 如果访问不了huggingface请设置镜像，下同
# export HF_ENDPOINT=https://hf-mirror.com
HIP_VISIBLE_DEVICES=0 PYTHONPATH=. python projects/llava_sam2/gradio/app.py ByteDance/Sa2VA-4B
```
ps：注意gradio版本，这里测试了4.44.0是没问题的。
方式二
通过脚本，输入为视频图片序列和文本指令：
```
# 图片序列
HIP_VISIBLE_DEVICES=5 python demo/demo.py data/GOT-10k_Test_000001/ --model_path ByteDance/Sa2VA-4B --work-dir OUTPUT_DIR --text "<image>Please describe the video content."
```
## result
<!-- 此处填算法效果测试图（包括输入、输出） -->
方式一
<div align=center>
<img src="./doc/webui.png" width=600/>
<div >推理结果</div>
</div>
<div align=center>
<img src="./doc/webui2.png" width=600/>
<div >推理结果</div>
</div>
方式二
输入视频序列为：data/GOT-10k_Test_000001；输入的指令提示为："<image>Please describe the video content."
<div align=center>
<img src="./data/GOT-10k_Test_000001/00000001.jpg" width=600/>
<div >GOT-10k_Test_000001</div>
</div>
<div align=center>
<img src="./doc/result.png" width=600/>
<div >推理结果</div>
</div>
### 精度
无。
<!-- | 加速卡 | lpips | clip sim |
| :-----| :----- | :---- |
| K100_AI | 0.115 | 0.977 | -->
<!-- | 单元格 | 单元格 | 单元格 | -->
## 应用场景
### 算法类别
<!-- 超出以上分类的类别命名也可参考此网址中的类别名：https://huggingface.co/ \ -->
`图像理解`
### 热点应用行业
<!-- 应用行业的填写需要做大量调研，从而为使用者提供专业、全面的推荐，除特殊算法，通常推荐数量>=3。 -->
`零售,制造,电商,医疗,教育`
## 源码仓库及问题反馈
<!-- - 此处填本项目gitlab地址 -->
- https://developer.sourcefind.cn/codes/modelzoo/sa2va_pytorch
## 参考资料
- https://github.com/magic-research/Sa2VA
README_origin.md 0 → 100644
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# Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos
[\[🏠 Sa2VA\]](https://lxtgh.github.io/project/sa2va) [\[📜 arXiv\]](https://arxiv.org/abs/2501.04001) [\[🤗 HuggingFace\]](https://huggingface.co/collections/ByteDance/sa2va-model-zoo-677e3084d71b5f108d00e093) [\[🎥 Introduction\]]() [\[🧑‍💻 GitHub\]](https://github.com/magic-research/Sa2VA) [\[Gradio Demo (Ours internal: Sa2VA-4B)\]](https://5512470799b6b35fbc.gradio.live/) [\[Gradio Demo (By HuggingFace Offical)\]](https://huggingface.co/spaces/fffiloni/Sa2VA-simple-demo) [\[🤖 Replicate Demo\]](https://replicate.com/bytedance)
[**Haobo Yuan**](https://yuanhaobo.me/)<sup>1*</sup> · [**Xiangtai Li**](https://lxtgh.github.io/)<sup>2*&dagger;</sup> · [**Tao Zhang**](https://zhang-tao-whu.github.io/)<sup>2,3*</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup> · [**Shilin Xu**](https://xushilin1.github.io/)<sup>4</sup> ·[**Shunping Ji**](https://scholar.google.com/citations?user=FjoRmF4AAAAJ&hl=en)<sup>3</sup> ·[**Yunhai Tong**](https://scholar.google.com/citations?user=T4gqdPkAAAAJ&hl=zh-CN)<sup>4</sup> ·
[**Lu Qi**](https://luqi.info/)<sup>2</sup> · [**Jiashi Feng**](https://scholar.google.com/citations?user=Q8iay0gAAAAJ&hl=en)<sup>2</sup> · [**Ming-Hsuan Yang**](https://faculty.ucmerced.edu/mhyang/)<sup>1</sup>
<sup>1</sup>UC Merced&emsp;&emsp;&emsp;&emsp;<sup>2</sup>ByteDance Seed&emsp;&emsp;&emsp;&emsp;<sup>3</sup>WHU&emsp;&emsp;&emsp;&emsp;<sup>4</sup>PKU
&dagger; project lead&emsp;* the first three authors equally contribute to the work.
![Teaser](assets/images/teaser.jpg)
## Opensource progress
- [ ] Release Qwen-VL related models. (To be done)
- [x] Release Open-sourced training datasets.
- [x] Release Ref-SAM-v dataset.
- [x] Release evaluation code for each dataset.
- [x] Release 1B,4B,8B, 26B model.
- [x] Release training code for 1b, 4b, 8b model.
- [x] Release inference and test code.
- [x] Release demo code.
## Overview
This repository contains the code for the paper "Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos".
Sa2VA is the first unified model for the dense grounded understanding of both images and videos. Unlike existing multi-modal large language models, which are often limited to specific modalities and tasks, Sa2VA supports a wide range of image and video tasks, including referring segmentation and conversation, with minimal one-shot instruction tuning. Sa2VA combines SAM-2, a foundation video segmentation model, with LLaVA, an advanced vision-language model, and unifies text, image, and video into a shared LLM token space.
## Model Zoo
We provide the following models:
| Model Name | Base MLLM | Language Part | HF Link |
|:----------:|:-----------------------------------------------------------------:|:-----------------------------------------------------------------------------:|:----------------------------------------------------:|
| Sa2VA-1B | [InternVL2.0-1B](https://huggingface.co/OpenGVLab/InternVL2-1B) | [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) | [🤗 link](https://huggingface.co/ByteDance/Sa2VA-1B) |
| Sa2VA-4B | [InternVL2.5-4B](https://huggingface.co/OpenGVLab/InternVL2_5-4B) | [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) | [🤗 link](https://huggingface.co/ByteDance/Sa2VA-4B) |
| Sa2VA-8B | [InternVL2.5-8B](https://huggingface.co/OpenGVLab/InternVL2_5-8B) | [internlm2_5-7b-chat](https://huggingface.co/internlm/internlm2_5-7b-chat) | [🤗 link](https://huggingface.co/ByteDance/Sa2VA-8B) |
| Sa2VA-26B | [InternVL2.5-26B](https://huggingface.co/OpenGVLab/InternVL2_5-26B) | [internlm2_5-7b-chat](https://huggingface.co/internlm/internlm2_5-20b-chat) | [🤗 link](https://huggingface.co/ByteDance/Sa2VA-26B) |
## 🤗 Gradio Demos
We provide a script that implements interactive chat using gradio, which requires installing `gradio==4.42.0`. You can try it to build a local chat interface quickly.
```shell
PYTHONPATH=. python projects/llava_sam2/gradio/app.py ByteDance/Sa2VA-4B
```
## 🚀 Quick Start
Our Sa2VA model is available on 🤗HuggingFace. With very few steps, you can try it with your own data. You can install the `demo/requirements.txt` to avoid training-only packages.
**Option1 - scripts:**
Supposing you have a folder (`PATH_TO_FOLDER`) that contains images of a video, you can use the following script to chat with the Sa2VA model or segment the objects in the videos.
```bash
> cd scripts
> python demo/demo.py PATH_TO_FOLDER --model_path ByteDance/Sa2VA-8B --work-dir OUTPUT_DIR --text "<image>Please describe the video content."
```
If the output contains the segmentation results, the results will be saved to `OUTPUT_DIR`.
**Option2 - Jupter Notebook:**
Please refer to `demo.ipynb`.
## 🎥 Demo
<details open>
<summary>Demo 1</summary>
Input Video (Source: La La Land 2016):
![Error](assets/videos/exp_1.gif)
Instruction: "Please segment the girl wearing the yellow dress."
</details>
<details open>
<summary>Demo 2</summary>
Input Video (Source: La La Land 2016):
![Error](assets/videos/exp_2.gif)
Instruction: "Please segment the main character."
</details>
<details open>
<summary>Demo 3</summary>
Input Video (Source: Internet):
![Error](assets/videos/apt_exp_1_all.gif)
Instruction: "Please segment the person wearing sun glasses."
</details>
<details open>
<summary>Demo 4</summary>
Input Video (Source: Internet):
![Error](assets/videos/apt_exp_2_all.gif)
Instruction: "Instruction: "Please segment the singing girl."
</details>
<details open>
<summary>Demo 5</summary>
Input Video:
![Error](assets/videos/gf_exp1.gif)
Instruction: "What is the atmosphere of the scene?"
Answer: "The scene has a dark and mysterious atmosphere, with the men dressed in suits and ties, and the dimly lit room."
</details>
## Training
<details open>
<summary>Installation</summary>
1. Please install the python and pytorch first:
```bash
> conda create -n vlm python=3.10
> conda activate vlm
> conda install pytorch==2.3.1 torchvision==0.18.1 pytorch-cuda=12.1 cuda -c pytorch -c "nvidia/label/cuda-12.1.0" -c "nvidia/label/cuda-12.1.1"
```
2. Install mmcv, we use 2.1.0 as default version:
```bash
> pip install mmcv==2.1.0 -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.3/index.html
```
3. Install other dependencies:
```bash
> pip install -r requirements.txt
```
</details>
Please make sure using the correct versions of transformers and peft.
<
<summary>Pretrained Model Preparation</summary>
You are expected to download the following pretrained models and place them in the `./pretrained` directory:
- [sam2_hiera_large.pt](https://huggingface.co/facebook/sam2-hiera-large)
- [InternVL2_5-4B](https://huggingface.co/OpenGVLab/InternVL2_5-4B)
You can download the remaining models from InternVL2.5 [huggingface collections](https://huggingface.co/collections/OpenGVLab/internvl25-673e1019b66e2218f68d7c1c).
```
./ # project root
pretrained/
├── sam2_hiera_large.pt
├── InternVL2_5-1B
├── InternVL2_5-4B
```
<details open>
<summary>Data Preparation</summary>
Please download the training datasets and place them in the `data` directory. The download link is [here](https://huggingface.co/datasets/Dense-World/Sa2VA-Training).
Please directly put the zip files into the `data` directory and unzip them. For example, you can download the `video_datas_mevis.zip` and unzip it in the `data` directory like:
```bash
> unzip video_datas_mevis.zip
```
The final data structure should be like:
```
data/
├── video_datas
| ├── revos
| ├── mevis
| └── davis17
| └── chat_univi # video-chat data
| └── sam_v_full # please download this from sam-2 offical repp.
| └── sam_v_final_v3.json
├── ref_seg
| ├── refclef
| ├── refcoco
| ├── refcoco+
| ├── refcocog
| ├──
├── glamm_data
| ├── images
| ├── annotations
├── osprey-724k
| ├── Osprey-724K
| ├── coco
├── llava_data
| ├── llava_images
| ├── LLaVA-Instruct-150K
| ├── LLaVA-Pretrain
```
`sam_v_full` is the SA-V dataset, which is not included in the download link. You can download it from [here](https://ai.meta.com/datasets/segment-anything-video/).
</details>
<details open>
<summary>Training Script</summary>
Please run the following script to train using 8 GPUS, we suggest using at least 8 A100 GPUs:
```bash
> bash tools/dist.sh train projects/llava_sam2/configs/sa2va_4b.py 8
```
</details>
<details open>
<summary>Convert trained model to huggingface format</summary>
Please run the following script to convert:
```bash
> python projects/llava_sam2/hf/convert_to_hf.py projects/llava_sam2/configs/sa2va_4b.py --pth-model PATH_TO_PTH_MODEL --save-path PATH_TO_SAVE_FOLDER
```
</details>
## References
If you find this repository useful, please consider referring to he following paper:
```
@article{sa2va,
title={Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of Images and Videos},
author={Yuan, Haobo and Li, Xiangtai and Zhang, Tao and Huang, Zilong and Xu, Shilin and Ji, Shunping and Tong, Yunhai and Qi, Lu and Feng, Jiashi and Yang, Ming-Hsuan},
journal={arXiv},
year={2025}
}
```
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